Each indicator has conditions to indicate a specific signal.
For example: the price above EMA 200 (condition) indicates a long-term uptrend (signal) or RSI below 30 (condition) indicates an oversold market (signal) or a double top pattern (condition) indicates a trend reversal to the downside (signal).
Signals are a basis for a trade. A trader goes through the list of signals that help them decide to buy/sell.
All our algorithms are heavily back-tested and forward-tested on Forex and Bitcoin. We test our algorithms on 67 Forex assets from 2000 or later depending on availability and Bitcoin from the year 2011.
It is 874 years of data in total (from 1-minute TF). Testing on such a vast dataset requires a lot of hardware—we have a dedicated server with powerful CPUs and GPUs installed just for the purpose of back-testing, forward-testing, and training.
A trader can base her decision on multiple time frames or indicators, all in one chart.
You can select a time frame of the chart on the left side and a time frame of the analysis on the right site. The analysis of the higher time frame is then plotted on the chart.
Raw market movement is based on many complex methods on top of the algorithm for identifying local maximas and minimas, such as breaking or/and smoothing specific patterns depending on context.
If the asset forms higher highs, it is an uptrend (green rectangle with the direction of a linear regression of closing prices) and vice versa—if the asset forms lower lows, it is a downtrend (red rectangle). If these patterns are broken, i.e., lower lows in an uptrend or higher highs in a downtrend, it is an accumulation or distribution phase (yellow rectangle).
Support and resistance levels are discovered on multiple time frames over the specified period. And based on the algorithm for calculating local maximas and minimas.
For example, if you select 1h TF, you will get support and resistance levels based on both 1h and 4h TFs. Here is the mapping of TFs 5m-1h, 15m-1h, 30m-2h, 1h-4h, 2h-4h, 3h-1d, 4h-1d, 6h-1d, 8h-1d, 12h-1d, 1d-1w, 3d-1w, 1w-1M.
The algorithm is based on the script from LonesomeTheBlue (tap here to open the script on TradingView).
We discover all lines with 3 or more reaction points.
The strength of a trend line is defined as a number of reactive points.
Tradiny recognizes harmonic patterns, trend reversal patterns, and continuation patterns:
-- gartley, bat, butterfly, crab, deep crab, shark, and cypher
-- double/triple tops/bottoms, head and shoulders
-- triangles, channels, wedges
Tradiny does also recognize candlestick patterns.
All patterns are identified based on advanced attributes, such as matrices, arithmetic functions and formulas, volatility coefficients, and Fibonacci retracements and extensions.
We are utilizing Pearson correlation and significance.
Google Trends show relative search interest in a topic compared to itself.
In the case that Forex AI does not have an opinion (it simply does not know), we do not show it in the list of indicators.
We strive to optimize Tradiny and employ high-performance servers. The period of 3-5 seconds consists of fetching the most recent/up-to-date data (typically a few hundreds of milliseconds, but sometimes seconds) and the rest is simply a lot of mathematical and statistical calculations (typically 1-3 seconds).
Note, it can take up to 20 seconds or more in some cases to calculate the analysis.
We built Tradiny to be as recent as possible. Depending on data source, chart data is either downloaded directly from exchanges into your phone (e.g., Binance, no delay), or it is downloaded from our data repository (e.g. FXCM, 0-15 sec delay).
Alerts are evaluated every 3 (best) - 15 (worst) seconds when Tradiny Premium is activated. All the data from exchanges are streamed directly to our servers and are analyzed/evaluated immediately.
Market stats, including my favorite markets, i.e. price and volume gainers, losers, volatility, and Google Trend's pageviews), are recalculated every hour. Correlations are recalculated every day.
We use FXCM and Binance.
3D chart enables you to utilize an extra dimension, observe the chart from different angles, discover 3D patterns such as spirals.
For example, the price forms spirals in an uptrend around a linear regression where the X axis is time, Y axis is price, and Z axis is MVWAP. A trader can assume another spiral movement around the linear regression or a breakout.
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Market conditions change quickly. It may happen that the pattern was invalidated while you were opening the chart so that you received a notification about a pattern, but you could not find it in the chart.
It also depends on volatility, which may change over time, and thus, some patterns or indicators may no longer match volatility-based rules.
If this happens often with your asset, configure to trigger the alert on closing prices.
Artificial intelligence predicts which levels will be broken or held based on raw market movement, EMA, HMA, MACD, Bollinger Bands, Keltner Bands, ATR, RSI, and Ichimoku cloud. The success rate is 73%.
The thick line labels the level to be broken or held. The rectangle below or above the thick line defines the price range where the prediction is valid. Note, there are 3 neural networks for time frames 1h, 4h, and 1d.
How to trade? Enter at a level in the rectangle where you expect that the price bounces away to the direction which is predicted by the AI. Exit when the price leaves the rectangle.
Success rate? The following table shows success rates on new data for particular probability or higher of neural network prediction. This means, for example, that if we select probabilities that are higher than 95%, we get the success rate of 78.64%.
Probability cond. | Success rate | All samples | Correct | Incorrect
> 95% | 78.64% | 75606 | 4076 | 1107
> 90% | 77.46% | 75606 | 7594 | 2210
> 85% | 76.79% | 75606 | 10774 | 3257
> 80% | 76.45% | 75606 | 13865 | 4272
> 75% | 76.21% | 75606 | 16774 | 5236
> 70% | 76.12% | 75606 | 19687 | 6175
> 65% | 75.98% | 75606 | 22515 | 7119
> 60% | 75.94% | 75606 | 25330 | 8024
> 55% | 75.84% | 75606 | 27935 | 8898
> 50% | 75.74% | 75606 | 30687 | 9828
> 45% | 75.63% | 75606 | 33364 | 10749
> 40% | 75.46% | 75606 | 35959 | 11694
> 35% | 75.23% | 75606 | 38448 | 12656
> 30% | 74.98% | 75606 | 40978 | 13676
> 25% | 74.76% | 75606 | 43442 | 14667
> 20% | 74.46% | 75606 | 45888 | 15738
> 15% | 74.28% | 75606 | 48402 | 16760
> 10% | 73.96% | 75606 | 50961 | 17945
> 5% | 73.71% | 75606 | 53424 | 19056
We trained the neural networks on 75% of our dataset (read more about our dataset below), which is around 655 years, and we test it on the other 25% (219 years)—this means that they achieved the success rate of 73% on new data.
We test our algorithms on 67 Forex assets from year 2000 or later depending on availability and Bitcoin from year 2011.
It is 874 years of data in total (from 1 minute TF). Testing on such a huge dataset requires a lot of hardware—we have a dedicated server with powerful CPUs and GPUs installed just for the purpose of backtesting, forwardtesting, and training.
Note also, the AI is trained mostly on the Forex data, which means that the success rate may differ if the AI is used on the other asset types (e.g., cryptocurrencies).